Large Language Models (LLMs)

Jul 6, 2026 · 49:59

Let's build an AI agent - Phil Nash - NDC Copenhagen 2026

This talk demystifies AI agents by building one from scratch, demonstrating how Large Language Models (LLMs), tools, and memory work together in a continuous loop to achieve goals. The core mechanism involves an agent runtime that orchestrates function calls—allowing the LLM to interact with external systems like file systems or calculators. Advanced concepts covered include the Model Context Protocol (MCP) for standardized tool interaction and 'Skills' for progressive disclosure of capabilities, enabling agents to perform complex tasks like self-refactoring.

Key takeaways

1. Agent Architecture 17:03

   An agent fundamentally runs tools in a loop to achieve a goal. This process requires an LLM, external tools (functions), and an orchestration layer (the 'harness') that manages the interaction.

2. The Agent Loop 28:10

   The core agent functionality is implemented in a loop: The model generates function calls $\rightarrow$ The harness executes those functions (awaiting results) $ ightarrow$ The results are fed back to the model for the next step, continuing until the goal is met.

3. Standardization via MCP 36:00

   The Model Context Protocol (MCP) provides a standardized way for agents to interact with services. It separates concerns into Server components (tools, resources, prompts) and Client components.

4. Progressive Disclosure with Skills 40:05

   Skills allow for progressive disclosure of capabilities. Instead of loading all tool declarations at once, the agent only loads a skill's header initially and can request more details (resources, scripts) as needed.

• NDC Conferences
• AI Agents
• Large Language Models (LLMs)
• Function Calling
• Retrieval Augmented Generation (RAG)
• Model Context Protocol (MCP)
• Software Architecture
• OpenRAG
• Dockling
• Gemini 3 Flash preview
• Langflow

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Jun 29, 2026 · 26:20

GLM-5.2 vs MiniMax-M3: Opus Has REAL COMPETITION (Model Stacking)

The video argues that proprietary models like Opus 4.8 face real competition from open-weight alternatives such as GLM-5.2 and MiniMax-M3. The core thesis for build engineers is not to select a single model but to implement a resilient 'model stack.' This strategy involves strategically choosing models across three tiers—State-of-the-Art (SOTA), Workhorse, and Lightweight/Local—to optimize the trade-off between performance, cost, and speed for both engineering agents and product deployment.

Key takeaways

1. GLM 5.2 vs MiniMax M3: Performance vs Cost 17:54

   GLM 5.2 is highlighted as the better model in terms of raw performance (A-tier), while MiniMax M3 is considered the better deal due to its optimized cost structure, making it ideal for high-volume product agents.

2. The Three-Tier Model Stack Framework 2:50

   Engineers should categorize models into three tiers: State-of-the-Art (e.g., Opus 4.8, Fable 5), Workhorse (GLM 5.2, MiniMax M3), and Lightweight/Local (Qwen 3.6). This framework guides decision-making based on the required trade-off.

3. Resilience through Open Weights 6:49

   Due to concerns about vendor lock-in or potential service shutdowns (e.g., Fable), relying solely on closed-source models is risky. Utilizing open-weight models like GLM 5.2 and MiniMax M3 ensures greater control and ownership over the AI infrastructure.

• IndyDevDan
• Large Language Models (LLMs)
• Agentic Engineering
• Model Stacking
• Open-Source AI
• Cost Optimization
• AI Infrastructure Architecture
• AA Article — GLM-5.2 is the new leading open-weights model...
• AA Article — MiniMax-M3:
• AA — Our 4-model comparison (intelligence vs tokens, open vs proprietary...)

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